G. Shaw and D. Manolakis, Signal processing for hyperspectral image exploitation, IEEE Signal Processing Magazine, vol.19, issue.1, pp.12-16, 2002.

D. Manolakis, D. Marden, and G. Shaw, Hyperspectral image processing for automatic target detection applications, Lincoln Laboratory Journal, vol.14, issue.1, pp.79-116, 2003.

D. G. Manolakis, R. B. Lockwood, and T. W. Cooley, Hyperspectral Imaging Remote Sensing: Physics, Sensors, and Algorithms, 2016.

G. Shaw and D. Manolakis, Signal processing for hyperspectral image exploitation, IEEE Signal Processing Magazine, vol.19, issue.1, pp.12-16, 2002.

L. Zhang, Q. Zhang, B. Du, X. Huang, Y. Y. Tang et al., Simultaneous spectral-spatial feature selection and extraction for hyperspectral images, IEEE Transactions on Cybernetics, vol.48, issue.1, pp.16-28, 2018.

L. Zhang, Q. Zhang, L. Zhang, D. Tao, X. Huang et al., Ensemble manifold regularized sparse low-rank approximation for multiview feature embedding, Discriminative Feature Learning from Big Data for Visual Recognition, vol.48, pp.3102-3112, 2015.

J. M. Bioucas-dias, A. Plaza, G. Camps-valls, P. Scheunders, N. Nasrabadi et al., Hyperspectral remote sensing data analysis and future challenges, IEEE Geoscience and Remote Sensing Magazine, vol.1, issue.2, pp.6-36, 2013.

A. Plaza, J. A. Benediktsson, J. W. Boardman, J. Brazile, L. Bruzzone et al., Recent advances in techniques for hyperspectral image processing, Imaging Spectroscopy Special Issue, vol.113, pp.110-122, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00178888

Q. Du, L. Zhang, B. Zhang, X. Tong, P. Du et al., Foreword to the special issue on hyperspectral remote sensing: Theory, methods, and applications, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.6, issue.2, pp.459-465, 2013.

P. D. Gader and J. Chanussot, Understanding hyperspectral image and signal processing, 2021.

J. A. Mauro-dalla-mura, J. Benediktsson, L. Chanussot, and . Bruzzone, The Evolution of the Morphological Profile: from Panchromatic to Hyperspectral Images, pp.123-146, 2011.

S. Prasad, L. M. Bruce, and J. Chanussot, Optical Remote Sensing: Advances in Signal Processing and Exploitation Techniques, Augmented Vision and Reality, 2011.

J. Chanussot, C. Collet, and K. Chehdi, Multivariate Image Processing, Augmented Vision and Reality, 2009.

, Joana Maria Frontera Pons, Robust target detection for Hyperspectral Imaging, 2014.

D. Manolakis, R. Lockwood, T. Cooley, and J. Jacobson, Is there a best hyperspectral detection algorithm?, Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, vol.733402, 2009.

A. Villa, J. Chanussot, J. A. Benediktsson, and C. Jutten, Unsupervised classification and spectral unmixing for sub-pixel labelling, 2011 IEEE International Geoscience and Remote Sensing Symposium, pp.71-74, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00696045

N. Yokoya, J. Chanussot, and A. Iwasaki, Nonlinear unmixing of hyperspectral data using semi-nonnegative matrix factorization, IEEE Transactions on Geoscience and Remote Sensing, vol.52, issue.2, pp.1430-1437, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01128460

A. Villa, J. Chanussot, J. A. Benediktsson, and C. Jutten, Spectral unmixing for the classification of hyperspectral images at a finer spatial resolution, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.3, pp.521-533, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00578890

G. A. Licciardi, A. Villa, M. M. Khan, and J. Chanussot, Image fusion and spectral unmixing of hyperspectral images for spatial improvement of classification maps, 2012 IEEE International Geoscience and Remote Sensing Symposium, pp.7290-7293, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00799684

N. K. Patel, C. Patnaik, S. Dutta, A. M. Shekh, and A. J. Dave, Study of crop growth parameters using airborne imaging spectrometer data, International Journal of Remote Sensing, vol.22, issue.12, pp.2401-2411, 2001.

B. Datt, T. R. Mcvicar, T. G. Van-niel, D. L. Jupp, and J. S. Pearlman, Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes, IEEE Transactions on Geoscience and Remote Sensing, vol.41, pp.1246-1259, 2003.

B. Hörig, F. Kühn, F. Oschütz, and F. Lehmann, HyMap hyperspectral remote sensing to detect hydrocarbons, International Journal of Remote Sensing, vol.22, pp.1413-1422, 2001.

D. Manolakis and G. Shaw, Detection algorithms for hyperspectral imaging applications, Signal Processing Magazine, IEEE, vol.19, issue.1, pp.29-43, 2002.

D. W. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. Schaum et al., Anomaly detection from hyperspectral imagery, IEEE Signal Processing Magazine, vol.19, issue.1, pp.58-69, 2002.

M. T. Eismann, A. D. Stocker, and N. M. Nasrabadi, Automated hyperspectral cueing for civilian search and rescue, Proceedings of the IEEE, vol.97, issue.6, pp.1031-1055, 2009.

D. Manolakis, E. Truslow, M. Pieper, T. Cooley, and M. Brueggeman, Detection algorithms in hyperspectral imaging systems: An overview of practical algorithms, IEEE Signal Processing Magazine, vol.31, issue.1, pp.24-33, 2014.

J. Frontera-pons, F. Pascal, and J. Ovarlez, Adaptive nonzero-mean Gaussian detection, IEEE Transactions on Geoscience and Remote Sensing, vol.55, issue.2, pp.1117-1124, 2017.

J. Frontera-pons, M. A. Veganzones, F. Pascal, and J. Ovarlez, Hyperspectral anomaly detectors using robust estimators, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.9, issue.2, pp.720-731, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01377668

J. Frontera-pons, J. Ovarlez, and F. Pascal, Robust anmf detection in noncentered impulsive background, IEEE Signal Processing Letters, vol.24, issue.12, pp.1891-1895, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01692403

J. Frontera-pons, M. A. Veganzones, S. Velasco-forero, F. Pascal, J. P. Ovarlez et al., Robust anomaly detection in hyperspectral imaging, 2014 IEEE Geoscience and Remote Sensing Symposium, pp.4604-4607, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01010418

R. M. Cavalli, G. A. Licciardi, and J. Chanussot, Detection of anomalies produced by buried archaeological structures using nonlinear principal component analysis applied to airborne hyperspectral image, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.6, issue.2, pp.659-669, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00798517

D. Manolakis, G. Shaw, and N. Keshava, Comparative analysis of hyperspectral adaptive matched filter detectors, Proc. SPIE 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, vol.2, 2000.

N. M. Nasrabadi, Regularized spectral matched filter for target recognition in hyperspectral imagery, IEEE Signal Processing Letters, vol.15, pp.317-320, 2008.

S. Kraut and L. L. Scharf, The CFAR adaptive subspace detector is a scale-invariant GLRT, IEEE Transactions on, vol.47, issue.9, pp.2538-2541, 1999.

E. J. Kelly, Aerospace and Electronic Systems, IEEE Transactions on, vol.23, issue.1, pp.115-127, 1986.

O. Ledoit and M. Wolf, A well-conditioned estimator for large-dimensional covariance matrices, Journal of Multivariate Analysis, vol.88, issue.2, pp.365-411, 2004.

O. Ledoit and M. Wolf, Honey, i shrunk the sample covariance matrix, UPF Economics and Business Working Paper, issue.691, 2003.

A. W. Bitar, J. Ovarlez, and L. Cheong, Sparsity-Based Cholesky Factorization and Its Application to Hyperspectral Anomaly Detection, IEEE Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01656893

Y. Chen, A. Wiesel, and A. O. Hero, Robust shrinkage estimation of high-dimensional covariance matrices, 2010 IEEE Sensor Array and Multichannel Signal Processing Workshop, pp.189-192, 2010.

F. Pascal and Y. Chitour, Shrinkage covariance matrix estimator applied to stap detection, 2014 IEEE Workshop on Statistical Signal Processing (SSP), pp.324-327, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01104073

F. Pascal, Y. Chitour, and Y. Quek, Generalized robust shrinkage estimator and its application to stap detection problem, IEEE Transactions on Signal Processing, vol.62, issue.21, pp.5640-5651, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01104004

G. Healey and D. Slater, Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions, IEEE Transactions on Geoscience and Remote Sensing, vol.37, issue.6, pp.2706-2717, 1999.

B. Thai and G. Healey, Invariant subpixel material detection in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.3, pp.599-608, 2002.

J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, Robust face recognition via sparse representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.2, pp.210-227, 2009.

R. Basri and D. W. Jacobs, Lambertian reflectance and linear subspaces, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.25, issue.2, pp.218-233, 2003.

J. K. Pillai, V. M. Patel, and R. Chellappa, Sparsity inspired selection and recognition of iris images, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp.1-6, 2009.

X. Hang and F. Wu, Sparse representation for classification of tumors using gene expression data, Journal of Biomedicine and Biotechnology, p.6, 2009.

Z. Guo, T. Wittman, and S. Osher, L1 unmixing and its application to hyperspectral image enhancement, 2009.

J. M. Bioucas-dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du et al., Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.5, issue.2, pp.354-379, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00760787

Y. Chen, N. M. Nasrabadi, and T. D. Tran, Sparse representation for target detection in hyperspectral imagery, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.3, pp.629-640, 2011.

Y. Zhang, B. Du, and L. Zhang, A sparse representation-based binary hypothesis model for target detection in hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.3, pp.1346-1354, 2015.

A. W. Bitar, L. Cheong, and J. Ovarlez, Target and background separation in hyperspectral imagery for automatic target detection, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1598-1602, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01773529

A. W. Bitar, L. Cheong, and J. Ovarlez, Sparse and low-rank matrix decomposition for automatic target detection in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.57, issue.8, pp.5239-5251, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02134179

E. J. Candès, X. Li, Y. Ma, and J. Wright, Robust principal component analysis?, J. ACM, vol.58, issue.3, p.37, 2011.

J. Wright, G. Arvind, R. Shankar, P. Yigang, and Y. Ma, Robust principal component analysis: Exact recovery of corrupted low-rank matrices via convex optimization, Advances in Neural Information Processing Systems, vol.22, pp.2080-2088, 2009.

S. Chen, S. Yang, K. Kalpakis, and C. Chang, Low-rank decomposition-based anomaly detection, Proc. SPIE 8743 Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX, pp.87430-87430, 2013.

Y. Zhang, B. Du, L. Zhang, and S. Wang, A low-rank and sparse matrix decomposition-based Mahalanobis distance method for hyperspectral anomaly detection, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.3, pp.1376-1389, 2016.

Y. Xu, Z. Wu, J. Li, A. Plaza, and Z. Wei, Anomaly detection in hyperspectral images based on low-rank and sparse representation, IEEE Transactions on Geoscience and Remote Sensing, vol.54, issue.4, 1990.

Y. Xu, Z. Wu, J. Chanussot, and Z. Wei, Joint reconstruction and anomaly detection from compressive hyperspectral images using mahalanobis distance-regularized tensor rpca, IEEE Transactions on Geoscience and Remote Sensing, vol.56, issue.5, pp.2919-2930, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01960847

Y. Xu, Z. Wu, J. Chanussot, M. Mura, A. L. Bertozzi et al., Low-rank decomposition and total variation regularization of hyperspectral video sequences, IEEE Transactions on Geoscience and Remote Sensing, vol.56, issue.3, pp.1680-1694, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01665298

A. W. Bitar, L. Cheong, and J. Ovarlez, Simultaneous sparsity-based binary hypothesis model for real hyperspectral target detection, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.4616-4620, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01692408

M. A. Veganzones, M. Simões, G. Licciardi, N. Yokoya, J. M. Bioucas-dias et al., Hyperspectral super-resolution of locally low rank images from complementary multisource data, IEEE Transactions on Image Processing, vol.25, issue.1, pp.274-288, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01010408

G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu et al., Robust recovery of subspace structures by low-rank representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.1, pp.171-184, 2013.

Z. Zhou, X. Li, J. Wright, E. Candès, and Y. Ma, Stable principal component pursuit, 2010 IEEE International Symposium on Information Theory, pp.1518-1522, 2010.

Z. Chen and D. P. Ellis, Speech enhancement by sparse, low-rank, and dictionary spectrogram decomposition, 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, pp.1-4, 2013.

P. Sun and J. Qin, Low-rank and sparsity analysis applied to speech enhancement via online estimated dictionary, IEEE Signal Processing Letters, vol.23, issue.12, pp.1862-1866, 2016.

J. Cai, E. J. Candès, and Z. Shen, A singular value thresholding algorithm for matrix completion, SIAM J. on Optimization, vol.20, issue.4, pp.1956-1982, 2010.

E. J. Candes and B. Recht, Exact low-rank matrix completion via convex optimization, 46th Annual Allerton Conference on Communication, Control, and Computing, pp.806-812, 2008.

A. S. Lewis, The mathematics of eigenvalue optimization, Mathematical Programming, vol.97, issue.1, pp.155-176, 2003.

G. A. Watson, Characterization of the subdifferential of some matrix norms, Linear Algebra and its Applications, vol.170, pp.33-45, 1992.

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Learn, vol.3, issue.1, pp.1-122, 2011.

J. Yang, W. Yin, Y. Zhang, and Y. Wang, A fast algorithm for edge-preserving variational multichannel image restoration, SIAM Journal on Imaging Sciences, vol.2, issue.2, pp.569-592, 2009.

G. A. Swayze, R. N. Clark, A. F. Goetz, K. E. Livo, G. N. Breit et al., Mapping advanced argillic alteration at cuprite, nevada, using imaging spectroscopy, Economic Geology, vol.109, issue.5, p.1179, 2014.

, Airbone Visible / Infrared Imaging Spectrometer

G. A. Swayze, R. N. Clark, A. F. Goetz, T. G. Chrien, and N. S. Gorelick, Effects of spectrometer band pass, sampling, and signal-to-noise ratio on spectral identification using the tetracorder algorithm, Journal of Geophysical Research: Planets, vol.108, issue.E9, p.5105, 2003.

R. N. Clark, G. A. Swayze, K. E. Livo, R. F. Kokaly, S. J. Sutley et al., Imaging spectroscopy: Earth and planetary remote sensing with the usgs tetracorder and expert systems, Journal of Geophysical Research: Planets, vol.108, issue.E12

A. Berk, L. Bernstein, and D. Robertson, MODTRAN: A moderate resolution model for LOWTRAN 7, 1989.

R. N. Clark, G. A. Swayze, A. J. Gallagher, T. V. King, and W. M. Calvin, The U. S. Geological Survey, Digital Spectral Library, 1993.

A. M. Baldridge, S. J. Hook, C. I. Grove, and G. Rivera, The ASTER Spectral Library Version 2.0, Remote Sensing of Environment, vol.113, pp.711-715, 2009.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), vol.58, issue.1, pp.267-288, 1996.

B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani, Least angle regression, The Annals of Statistics, vol.32, issue.2, pp.407-451, 2004.

S. K. Shevade and S. S. Keerthi, A simple and efficient algorithm for gene selection using sparse logistic regression, Bioinformatics, vol.19, issue.17, pp.2246-2253, 2003.

A. Beck and M. Teboulle, A fast iterative shrinkage-thresholding algorithm for linear inverse problems, SIAM J. Img. Sci, vol.2, issue.1, pp.183-202, 2009.

S. J. Wright, R. D. Nowak, and M. A. Figueiredo, Sparse reconstruction by separable approximation, IEEE Transactions on Signal Processing, vol.57, issue.7, pp.2479-2493, 2009.

J. Ye and J. Liu, Sparse methods for biomedical data, SIGKDD Explor. Newsl, vol.14, issue.1, pp.4-15, 2012.

E. J. Candès, M. B. Wakin, and S. P. Boyd, Enhancing sparsity by reweighted l 1 minimization, Journal of Fourier Analysis and Applications, vol.14, issue.5, pp.877-905, 2008.

T. Zhang, Analysis of multi-stage convex relaxation for sparse regularization, J. Mach. Learn. Res, vol.11, pp.1081-1107, 2010.

T. Zhang, Multi-stage convex relaxation for feature selection, Bernoulli, vol.19, issue.5B, p.2013

S. Foucart and M. Lai, Sparsest solutions of underdetermined linear systems via l q -minimization for 0 < q < 1, Applied and Computational Harmonic Analysis, vol.26, issue.3, pp.395-407, 2009.

J. Fan and R. Li, Variable selection via nonconcave penalized likelihood and its oracle properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-1360, 2001.

E. Candès, M. B. Wakin, and S. P. Boyd, Enhancing sparsity by reweighted l1 minimization, Journal of Fourier Analysis and Applications, vol.14, issue.5, pp.877-905, 2008.

C. Zhang, Nearly unbiased variable selection under minimax concave penalty, Ann. Statist, vol.38, issue.2, pp.894-942, 2010.

D. Geman and C. Yang, Nonlinear image recovery with half-quadratic regularization, IEEE Transactions on Image Processing, vol.4, issue.7, pp.932-946, 1995.

J. Trzasko and A. Manduca, Relaxed conditions for sparse signal recovery with general concave priors, IEEE Transactions on Signal Processing, vol.57, issue.11, pp.4347-4354, 2009.

P. Gong, J. Ye, and C. Zhang, Multi-stage multi-task feature learning, Advances in Neural Information Processing Systems, vol.25, pp.1988-1996, 2012.